The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Reduced pediatric urgent asthma utilization and exacerbations during the COVID‐19 pandemic
This article has 5 authors:Reviewed by ScreenIT
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Covid-19 Reinfection: A Rapid Systematic Review of Case Reports and Case Series
This article has 4 authors:Reviewed by ScreenIT
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Accuracy of saliva and nasopharyngeal sampling for detection of SARS-CoV-2 in community screening: a multicentric cohort study
This article has 22 authors:Reviewed by ScreenIT
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Pharmacovigilance Analysis on Cerebrovascular Accidents and Coronavirus disease 2019 Vaccines
This article has 1 author:Reviewed by ScreenIT
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Anti-Spike Protein Assays to Determine SARS-CoV-2 Antibody Levels: a Head-to-Head Comparison of Five Quantitative Assays
This article has 10 authors:Reviewed by ScreenIT
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Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity
This article has 11 authors:Reviewed by ScreenIT
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Interpreting vaccine efficacy trial results for infection and transmission
This article has 2 authors:Reviewed by ScreenIT
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Race-ethnicity and COVID-19 Vaccination Beliefs and Intentions: A Cross-Sectional Study among the General Population in the San Francisco Bay Area
This article has 16 authors:Reviewed by ScreenIT
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Addressing racial/ethnic disparities in the COVID-19 vaccination campaign
This article has 3 authors:Reviewed by ScreenIT
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Differential Dynamic Behavior of Prefusion Spike Proteins of SARS Coronaviruses 1 and 2
This article has 6 authors:Reviewed by ScreenIT